herogary

Maybe for the convenience of mobile usage?

Unlike images, audio signals are time-dependent and have complex temporal dynamics, making it more challenging to generate realistic synthetic data that captures the nuances of real-world audio. Meanwhile, the complex nature of audio signals, the scarcity of high-quality training data, and the subjective evaluation of audio quality collectively contribute to the ongoing challenges in building near-flawless audio separation models.

Thank you for providing feedback. We will continue to optimize and improve the model.

Yes, the Demucs mixing model has excellent SNR performance, but it is computationally intensive. It also incorporates a random mechanism, so each time it produces different spectrograms.

Yes, it's a major 7th chord.

It looks like a DAW, and I'm not very familiar with their source separation. The technology we use has been publicly released on GitHub

Thank you for your feedback. Could you please email us the information of your phone model and system version? We will investigate promptly. In the meantime, you can try exiting the program and re-entering to see if that helps. Please also check your network connection.

Source separation is commonly done by applying masks to the spectrogram. Deep learning is used to train the mask masks for different instruments' parameters. As you mentioned, this is the approach we will follow in the subsequent steps.

Thank you for raising the issue. We are continuously optimizing our model, and we are also constantly gathering various UI and business-related bugs. We will continue to optimize and resolve them in the future.

The testing model for guitar separation is currently under development. The test results are somewhat unsatisfactory due to the significant variations in guitar instrument tones, especially for electric guitars. This adds to the difficulty of training

We are in the process of adding the lyrics editing feature, as well as chord and rhythm types. It will be released soon.

Thanks for sharing your experience. We appreciate the feedback. It's clear that improving accuracy, especially with chords and tabs, is a priority for us. We're committed to enhancing the accuracy of our tool to meet your expectations and provide a more valuable experience.

Thanks for your feedback. We're currently in the process of adjusting our dataset and model to address issues with chords and rhythm. We're looking forward to providing you with a better experience in the future.

We're about to roll out features for chord and lyric modifications, and we'll continue to optimize the model going forward.

The SDR is 6.3

The data sources come from YouTube or user-uploaded audio, and the lyrics are extracted from the audio using AI models.

In recent years, there has been substantial advancement in vocoders for DL audio applications. WaveGAN and MelGAN have emerged as promising solutions, harnessing the power of generative adversarial networks (GANs) to produce high-fidelity audio. Furthermore, parallel-waveGAN and HiFi-GAN have showcased improved efficiency with quicker inference times while maintaining exceptional audio quality.